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Skill assessment and sources of predictability for the leading modes of sub-seasonal Eastern Africa short rains variability

de Andrade, F. M., Hirons, L. C. ORCID: and Woolnough, S. J. ORCID: (2024) Skill assessment and sources of predictability for the leading modes of sub-seasonal Eastern Africa short rains variability. Climate Dynamics. ISSN 1432-0894

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To link to this item DOI: 10.1007/s00382-024-07244-9


Understanding how models represent sub-seasonal rainfall variations and what influences model skill is essential for improving sub-seasonal forecasts and their applications. Here, empirical orthogonal function (EOF) analysis is employed to investigate weekly Eastern Africa short rains variability from October to December. The observed leading EOF modes are identified as (i) a monopole-like rainfall pattern with anomalies impacting southern Ethiopia, Kenya, and northern Tanzania; and (ii) a dipole-like rainfall pattern with contrasting anomalies between Tanzania and the northeastern sector of Eastern Africa. An examination of the links between the leading modes and specific climate drivers, namely, the Madden–Julian Oscillation (MJO), El Niño–Southern Oscillation, and Indian Ocean Dipole (IOD), shows that the MJO and IOD have the highest correlations with the two rainfall modes and indicates that the monopole (dipole)-like rainfall pattern is associated with MJO convective anomalies in the tropical Indian Ocean and western Pacific (Maritime Continent and Western Hemisphere). Assessments of model ability to capture and predict the leading modes show that the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office models outperform the National Centers for Environmental Prediction model at forecast horizons from one to four weeks ahead. Amongst the drivers examined, the MJO has the largest impact on the forecast skill of rainfall modes within the ECMWF model. If MJO-related variability is reliably represented, the ECMWF model is more skilful at predicting the main modes of weekly rainfall variability over the region. Our findings can support model developments and enhance anticipatory planning efforts in several sectors, such as agriculture, food security, and energy.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:116132

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